The measured data are complemented by Monte Carlo simulations implementing a realistic GEANT3 [1]} model of the ALICE detector and mimicking the experimental conditions present during the data taking.
From these simulations, information about efficiency, secondary contamination, and smearing of \(N_{\mathrm {ch}}\)  as well as \(p_{\mathrm {T}}\)  is obtained.
However, it was found in previous measurements [2]}, [3]} that the current state-of-the-art MC event generators do not perfectly reproduce the relative particle abundances, in particular for hyperons.
Since the detector response, as well as the effect of the track selection, vary for the different particle species (e.g. due to different lifetimes), a purely MC based correction for efficiency and feed-down contamination of inclusive charged particles would depend on the accuracy of the relative hadron abundances produced by the respective underlying event generator.
To take this effect into account, the particle abundances from the event generator are re-weighted by means of a data-driven approach that was already employed in other ALICE analyses [4]}, [5]}.
This particle-composition correction utilises several ALICE measurements of identified (\(\pi , \textrm {K}, \textrm {p}, \Lambda \) ) particle \(p_{\mathrm {T}}\)  spectra as a function of multiplicity (in coarse intervals) for a range of collision systems [3]}, [7]}, [8]}, [9]}, [10]} as input and offers \(N_{\mathrm {ch}}\) - and \(p_{\mathrm {T}}\) -dependent correction factors for particle abundances.
These data-driven adjustments for the generator bias result in a more accurate description of the detector performance and are applied prior to the unfolding corrections described in the following.
